• Laser & Optoelectronics Progress
  • Vol. 58, Issue 12, 1210022 (2021)
Quan Wang1 and Benshun Yi2、*
Author Affiliations
  • 1FiberHome Technologies Group, Wuhan, Hubei 430074, China
  • 2Wuhan University, Wuhan, Hubei 430072, China
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    DOI: 10.3788/LOP202158.1210022 Cite this Article Set citation alerts
    Quan Wang, Benshun Yi. Insulator Defect Recognition in Aerial Images Based on Gaussian YOLOv3[J]. Laser & Optoelectronics Progress, 2021, 58(12): 1210022 Copy Citation Text show less
    References

    [1] LeCun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 521, 436-444(2015).

    [2] Sampedro C, Rodriguez-Vazquez J, Rodriguez-Ramos A et al. Deep learning-based system for automatic recognition and diagnosis of electrical insulator strings[J]. IEEE Access, 7, 101283-101308(2019). http://ieeexplore.ieee.org/document/8772209

    [3] Xie X L, Li C X, Yang X G et al. Dynamic receptive field-based object detection in aerial imaging[J]. Acta Optica Sinica, 40, 0415001(2020).

    [4] Kang G Q, Gao S B, Yu L et al. Deep architecture for high-speed railway insulator surface defect detection: denoising autoencoder with multitask learning[J]. IEEE Transactions on Instrumentation and Measurement, 68, 2679-2690(2019). http://ieeexplore.ieee.org/document/8516370

    [5] Guo T, Yang H, Shi L et al. Self-explosion defect identification of insulator based on Faster RCNN[J]. Insulators and Surge Arresters, 183-189(2019).

    [6] Cheng H Y, Zhai Y J, Chen R. Faster R-CNN based recognition of insulators in aerial images[J]. Modern Electronics Technique, 42, 98-102(2019).

    [7] Shen X H, Li Z H, Li M et al. Aluminum surface-defect detection based on multi-task deep learning[J]. Laser & Optoelectronics Progress, 57, 101501(2020).

    [8] Wang W G, Tian B, Liu Y et al. Study on the electrical devices detection in UAV images based on region based convolutional neural networks[J]. Journal of Geo-Information Science, 19, 256-263(2017).

    [9] Ren S Q, He K M, Girshick R et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 1137-1149(2017).

    [10] Gao J F, Lü Y H. Research on recognition and segmentation of insulator strings in aerial images[J]. Journal of Zhengzhou University (Natural Science Edition), 51, 16-22(2019).

    [11] Wu T, Wang W B, Yu L et al. Insulator defect detection method for lightweight YOLOv3[J]. Computer Engineering, 45, 275-280(2019).

    [12] Yan H W, Chen J X. Insulator string positioning and state recognition method based on improved YOLOv3 algorithm[J]. High Voltage Engineering, 46, 423-432(2020).

    [13] Lai Q P, Yang J, Tan B D et al. An automatic recognition and defect diagnosis model of transmission line insulator based on YOLOv2 network[J]. Electric Power, 52, 31-39(2019).

    [14] Redmon J, Farhadi A. YOLO9000: better, faster, stronger[C]. //2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA, 6517-6525(2017).

    [15] Lin Z C, Miao X R, Jiang H et al. Multi-model fusion of transmission line insulator self-explosion fault detection[J]. Journal of Fuzhou University (Natural Science Edition), 48, 217-223(2020).

    [16] Du X Y, Zhong J. Insulator image segmentation based on improved unit-linking pulse-coupled neural network[J]. Laser & Optoelectronics Progress, 56, 151005(2019).

    [17] Choi J, Chun D, Kim H et al. Gaussian YOLOv3: an accurate and fast object detector using localization uncertainty for autonomous driving[C]. //2019 IEEE/CVF International Conference on Computer Vision (ICCV), October 27-November 2, 2019, Seoul, Korea (South), 502-511(2019).

    [18] Redmon J, Farhadi A. YOLOv3: an incremental improvement[EB/OL]. (2018-04-08)[2020-08-20]. https://arxiv.org/abs/1804.02767

    [19] Ronneberger O, Fischer P, Brox T. U-net:convolutional networks for biomedical image segmentation[M]. //Navab N, Hornegger J, Wells W M, et al. Medical image computing and computer-assisted intervention-MICCAI 2015. Lecture notes in computer science, 9351, 234-241(2015).

    [20] Wu Z F, Shen C H, van den Hengel A. Wider or deeper: revisiting the ResNet model for visual recognition[J]. Pattern Recognition, 90, 119-133(2019).

    [21] Lin T Y, Dollár P, Girshick R et al. Feature pyramid networks for object detection[C]. //2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA., 936-944(2017).

    [22] Weiss K, Khoshgoftaar T M, Wang D D. A survey of transfer learning[J]. Journal of Big Data, 3, 1-40(2016). http://link.springer.com/article/10.1186/s40537-016-0043-6

    [23] Li W T, Jiao D, Zhang Q et al. Research on intelligent cognition method of self-exploding state of glass insulator based on deep migration learning[J]. Proceedings of the CSEE, 40, 3710-3721(2020).

    Quan Wang, Benshun Yi. Insulator Defect Recognition in Aerial Images Based on Gaussian YOLOv3[J]. Laser & Optoelectronics Progress, 2021, 58(12): 1210022
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